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Tianbao Yang
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2020 – today
- 2024
- [c132]Zi-Hao Qiu, Siqi Guo, Mao Xu, Tuo Zhao, Lijun Zhang, Tianbao Yang:
To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO. ICML 2024 - [c131]Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying:
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms. ICML 2024 - [c130]Yongjian Zhong
, Hieu Vu
Efficient and Effective Implicit Dynamic Graph Neural Network. KDD 2024: 4595-4606 - [c129]Haoran Liu
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks. WWW (Companion Volume) 2024: 485-488 - [i125]Zi-Hao Qiu, Siqi Guo, Mao Xu, Tuo Zhao, Lijun Zhang, Tianbao Yang:
To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO. CoRR abs/2404.04575 (2024) - [i124]Quanqi Hu, Qi Qi, Zhaosong Lu, Tianbao Yang:
Single-loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions. CoRR abs/2405.18577 (2024) - [i123]Ilgee Hong, Zichong Li, Alexander Bukharin, Yixiao Li, Haoming Jiang, Tianbao Yang, Tuo Zhao:
Adaptive Preference Scaling for Reinforcement Learning with Human Feedback. CoRR abs/2406.02764 (2024) - [i122]Qi Qi, Quanqi Hu, Qihang Lin, Tianbao Yang:
Provable Optimization for Adversarial Fair Self-supervised Contrastive Learning. CoRR abs/2406.05686 (2024) - [i121]Yongjian Zhong, Hieu Vu, Tianbao Yang, Bijaya Adhikari:
Efficient and Effective Implicit Dynamic Graph Neural Network. CoRR abs/2406.17894 (2024) - [i120]Xiyuan Wei, Fanjiang Ye, Ori Yonay, Xingyu Chen, Baixi Sun, Dingwen Tao, Tianbao Yang:
FastCLIP: A Suite of Optimization Techniques to Accelerate CLIP Training with Limited Resources. CoRR abs/2407.01445 (2024) - [i119]Gang Li, Qihang Lin, Ayush Ghosh, Tianbao Yang:
Multi-Output Distributional Fairness via Post-Processing. CoRR abs/2409.00553 (2024) - [i118]Gang Li, Wendi Yu, Yao Yao, Wei Tong, Yingbin Liang, Qihang Lin, Tianbao Yang:
Model Developmental Safety: A Safety-Centric Method and Applications in Vision-Language Models. CoRR abs/2410.03955 (2024) - [i117]Zhishuai Guo, Tianbao Yang:
Communication-Efficient Federated Group Distributionally Robust Optimization. CoRR abs/2410.06369 (2024) - [i116]Bokun Wang, Yunwen Lei, Yiming Ying, Tianbao Yang:
On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning. CoRR abs/2410.09156 (2024) - [i115]Ryan King, Shivesh Kodali, Conrad Krueger, Tianbao Yang, Bobak J. Mortazavi:
An Efficient Contrastive Unimodal Pretraining Method for EHR Time Series Data. CoRR abs/2410.09199 (2024) - [i114]Shantanu Thorat, Tianbao Yang:
Which LLMs are Difficult to Detect? A Detailed Analysis of Potential Factors Contributing to Difficulties in LLM Text Detection. CoRR abs/2410.14875 (2024) - 2023
- [j26]Tianbao Yang
AUC Maximization in the Era of Big Data and AI: A Survey. ACM Comput. Surv. 55(8): 172:1-172:37 (2023) - [j25]Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang:
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning. J. Mach. Learn. Res. 24: 145:1-145:46 (2023) - [j24]Zhishuai Guo, Yan Yan
Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition. J. Mach. Learn. Res. 24: 148:1-148:63 (2023) - [j23]Qi Qi, Jiameng Lyu, Kung-Sik Chan, Er-Wei Bai, Tianbao Yang:
Stochastic Constrained DRO with a Complexity Independent of Sample Size. Trans. Mach. Learn. Res. 2023 (2023) - [j22]Qi Qi, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang:
Attentional-Biased Stochastic Gradient Descent. Trans. Mach. Learn. Res. 2023 (2023) - [c128]Yao Yao, Qihang Lin, Tianbao Yang:
Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. AISTATS 2023: 10324-10342 - [c127]Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang:
FeDXL: Provable Federated Learning for Deep X-Risk Optimization. ICML 2023: 11934-11966 - [c126]Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo, Lijun Zhang, Tianbao Yang:
Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization. ICML 2023: 13550-13583 - [c125]Wei Jiang, Jiayu Qin, Lingyu Wu, Changyou Chen, Tianbao Yang, Lijun Zhang:
Learning Unnormalized Statistical Models via Compositional Optimization. ICML 2023: 15105-15124 - [c124]Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou:
Generalization Analysis for Contrastive Representation Learning. ICML 2023: 19200-19227 - [c123]Zi-Hao Qiu, Quanqi Hu, Zhuoning Yuan, Denny Zhou, Lijun Zhang, Tianbao Yang:
Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization. ICML 2023: 28389-28421 - [c122]Dixian Zhu, Bokun Wang, Zhi Chen, Yaxing Wang, Milan Sonka, Xiaodong Wu, Tianbao Yang:
Provable Multi-instance Deep AUC Maximization with Stochastic Pooling. ICML 2023: 43205-43227 - [c121]Dixian Zhu, Yiming Ying, Tianbao Yang:
Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity. ICML 2023: 43289-43325 - [c120]Zhuoning Yuan
LibAUC: A Deep Learning Library for X-Risk Optimization. KDD 2023: 5487-5499 - [c119]Ryan King, Tianbao Yang, Bobak J. Mortazavi:
Multimodal Pretraining of Medical Time Series and Notes. ML4H@NeurIPS 2023: 244-255 - [c118]Lijun Zhang, Peng Zhao, Zhen-Hua Zhuang, Tianbao Yang, Zhi-Hua Zhou:
Stochastic Approximation Approaches to Group Distributionally Robust Optimization. NeurIPS 2023 - [c117]Bang An, Xun Zhou, Yongjian Zhong, Tianbao Yang:
SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data. NeurIPS 2023 - [c116]Quanqi Hu, Dixian Zhu, Tianbao Yang:
Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization. NeurIPS 2023 - [c115]Gang Li, Wei Tong, Tianbao Yang:
Maximization of Average Precision for Deep Learning with Adversarial Ranking Robustness. NeurIPS 2023 - [c114]Xinwen Zhang, Yihan Zhang, Tianbao Yang, Richard Souvenir, Hongchang Gao:
Federated Compositional Deep AUC Maximization. NeurIPS 2023 - [i113]Lijun Zhang, Peng Zhao, Tianbao Yang, Zhi-Hua Zhou:
Stochastic Approximation Approaches to Group Distributionally Robust Optimization. CoRR abs/2302.09267 (2023) - [i112]Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou:
Generalization Analysis for Contrastive Representation Learning. CoRR abs/2302.12383 (2023) - [i111]Xinwen Zhang, Yihan Zhang, Tianbao Yang, Richard Souvenir, Hongchang Gao:
Federated Compositional Deep AUC Maximization. CoRR abs/2304.10101 (2023) - [i110]Dixian Zhu, Bokun Wang, Zhi Chen, Yaxing Wang, Milan Sonka, Xiaodong Wu, Tianbao Yang:
Provable Multi-instance Deep AUC Maximization with Stochastic Pooling. CoRR abs/2305.08040 (2023) - [i109]Zi-Hao Qiu, Quanqi Hu, Zhuoning Yuan, Denny Zhou, Lijun Zhang, Tianbao Yang:
Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization. CoRR abs/2305.11965 (2023) - [i108]Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo, Lijun Zhang, Tianbao Yang:
Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization. CoRR abs/2305.18730 (2023) - [i107]Zhuoning Yuan, Dixian Zhu, Zi-Hao Qiu, Gang Li, Xuanhui Wang, Tianbao Yang:
LibAUC: A Deep Learning Library for X-Risk Optimization. CoRR abs/2306.03065 (2023) - [i106]Wei Jiang, Jiayu Qin, Lingyu Wu, Changyou Chen, Tianbao Yang, Lijun Zhang:
Learning Unnormalized Statistical Models via Compositional Optimization. CoRR abs/2306.07485 (2023) - [i105]Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying:
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms. CoRR abs/2307.03357 (2023) - [i104]Haoran Liu, Bokun Wang, Jianling Wang, Xiangjue Dong, Tianbao Yang, James Caverlee:
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks. CoRR abs/2308.15614 (2023) - [i103]Bang An, Xun Zhou, Yongjian Zhong, Tianbao Yang:
SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data. CoRR abs/2310.00270 (2023) - [i102]Quanqi Hu, Dixian Zhu
Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization. CoRR abs/2310.03234 (2023) - [i101]Jianzhi Xv, Gang Li, Tianbao Yang:
AUC-mixup: Deep AUC Maximization with Mixup. CoRR abs/2310.11693 (2023) - [i100]Bokun Wang, Tianbao Yang:
ALEXR: Optimal Single-Loop Algorithms for Convex Finite-Sum Coupled Compositional Stochastic Optimization. CoRR abs/2312.02277 (2023) - [i99]Ryan King, Tianbao Yang, Bobak Mortazavi:
Multimodal Pretraining of Medical Time Series and Notes. CoRR abs/2312.06855 (2023) - 2022
- [j21]Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang:
Weakly-convex-concave min-max optimization: provable algorithms and applications in machine learning. Optim. Methods Softw. 37(3): 1087-1121 (2022) - [j20]Qingqing Hong, Xinyi Zhong, Weitong Chen
SATNet: A Spatial Attention Based Network for Hyperspectral Image Classification. Remote. Sens. 14(22): 5902 (2022) - [c113]Guanghui Wang, Ming Yang, Lijun Zhang, Tianbao Yang:
Momentum Accelerates the Convergence of Stochastic AUPRC Maximization. AISTATS 2022: 3753-3771 - [c112]Zhuoning Yuan, Zhishuai Guo, Nitesh V. Chawla, Tianbao Yang:
Compositional Training for End-to-End Deep AUC Maximization. ICLR 2022 - [c111]Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang:
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization. ICML 2022: 10195-10216 - [c110]Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong, Lijun Zhang, Tianbao Yang:
Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence. ICML 2022: 18122-18152 - [c109]Bokun Wang, Tianbao Yang:
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications. ICML 2022: 23292-23317 - [c108]Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji:
GraphFM: Improving Large-Scale GNN Training via Feature Momentum. ICML 2022: 25684-25701 - [c107]Zhuoning Yuan, Yuexin Wu, Zi-Hao Qiu, Xianzhi Du, Lijun Zhang, Denny Zhou, Tianbao Yang:
Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance. ICML 2022: 25760-25782 - [c106]Lijun Zhang, Guanghui Wang, Jinfeng Yi, Tianbao Yang:
A Simple yet Universal Strategy for Online Convex Optimization. ICML 2022: 26605-26623 - [c105]Dixian Zhu, Gang Li, Bokun Wang, Xiaodong Wu, Tianbao Yang:
When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee. ICML 2022: 27548-27573 - [c104]Quanqi Hu, Yongjian Zhong, Tianbao Yang:
Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization. NeurIPS 2022 - [c103]Wei Jiang, Gang Li, Yibo Wang, Lijun Zhang, Tianbao Yang:
Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization. NeurIPS 2022 - [c102]Yao Yao, Qihang Lin, Tianbao Yang:
Large-scale Optimization of Partial AUC in a Range of False Positive Rates. NeurIPS 2022 - [c101]Lijun Zhang, Wei Jiang, Jinfeng Yi, Tianbao Yang:
Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor. NeurIPS 2022 - [i98]Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang:
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization. CoRR abs/2202.07530 (2022) - [i97]Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong, Lijun Zhang, Tianbao Yang:
Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence. CoRR abs/2202.12183 (2022) - [i96]Zhuoning Yuan, Yuexin Wu, Zi-Hao Qiu, Xianzhi Du, Lijun Zhang, Denny Zhou, Tianbao Yang:
Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance. CoRR abs/2202.12387 (2022) - [i95]Bokun Wang, Tianbao Yang:
Finite-Sum Compositional Stochastic Optimization: Theory and Applications. CoRR abs/2202.12396 (2022) - [i94]Dixian Zhu
When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee. CoRR abs/2203.00176 (2022) - [i93]Yao Yao, Qihang Lin, Tianbao Yang:
Large-scale Optimization of Partial AUC in a Range of False Positive Rates. CoRR abs/2203.01505 (2022) - [i92]Dixian Zhu
Benchmarking Deep AUROC Optimization: Loss Functions and Algorithmic Choices. CoRR abs/2203.14177 (2022) - [i91]Tianbao Yang, Yiming Ying:
AUC Maximization in the Era of Big Data and AI: A Survey. CoRR abs/2203.15046 (2022) - [i90]Lijun Zhang, Wei Jiang, Jinfeng Yi, Tianbao Yang:
Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor. CoRR abs/2205.00741 (2022) - [i89]Quanqi Hu, Yongjian Zhong, Tianbao Yang:
Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization. CoRR abs/2206.00260 (2022) - [i88]Tianbao Yang:
Algorithmic Foundation of Deep X-Risk Optimization. CoRR abs/2206.00439 (2022) - [i87]Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji:
GraphFM: Improving Large-Scale GNN Training via Feature Momentum. CoRR abs/2206.07161 (2022) - [i86]Wei Jiang, Gang Li, Yibo Wang, Lijun Zhang, Tianbao Yang:
Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization. CoRR abs/2207.08540 (2022) - [i85]Qi Qi, Jiameng Lyu, Kung-Sik Chan, Er-Wei Bai, Tianbao Yang:
Stochastic Constrained DRO with a Complexity Independent of Sample Size. CoRR abs/2210.05740 (2022) - [i84]Qi Qi, Shervin Ardeshir, Yi Xu, Tianbao Yang:
Fairness via Adversarial Attribute Neighbourhood Robust Learning. CoRR abs/2210.06630 (2022) - [i83]Zhishuai Guo, Rong Jin, Jiebo Luo
FedX: Federated Learning for Compositional Pairwise Risk Optimization. CoRR abs/2210.14396 (2022) - [i82]Yao Yao, Qihang Lin, Tianbao Yang:
Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. CoRR abs/2212.12603 (2022) - 2021
- [j19]Yaohui Zeng, Tianbao Yang, Patrick Breheny
Hybrid safe-strong rules for efficient optimization in lasso-type problems. Comput. Stat. Data Anal. 153: 107063 (2021) - [j18]Mingrui Liu, Hassan Rafique, Qihang Lin, Tianbao Yang:
First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems. J. Mach. Learn. Res. 22: 169:1-169:34 (2021) - [c100]Zhuoning Yuan, Yan Yan, Milan Sonka, Tianbao Yang:
Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification. ICCV 2021: 3020-3029 - [c99]Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying:
Stability and Generalization of Stochastic Gradient Methods for Minimax Problems. ICML 2021: 6175-6186 - [c98]Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang:
Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity. ICML 2021: 12219-12229 - [c97]Qi Qi, Youzhi Luo, Zhao Xu, Shuiwang Ji, Tianbao Yang:
Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence. NeurIPS 2021: 1752-1765 - [c96]Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang:
An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives. NeurIPS 2021: 10067-10080 - [c95]Lijun Zhang, Wei Jiang, Shiyin Lu, Tianbao Yang:
Revisiting Smoothed Online Learning. NeurIPS 2021: 13599-13612 - [c94]Zhenhuan Yang, Yunwen Lei, Puyu Wang, Tianbao Yang, Yiming Ying:
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning. NeurIPS 2021: 20160-20171 - [c93]Guanghui Wang, Yuanyu Wan, Tianbao Yang, Lijun Zhang:
Online Convex Optimization with Continuous Switching Constraint. NeurIPS 2021: 28636-28647 - [i81]Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang:
Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity. CoRR abs/2102.04635 (2021) - [i80]Lijun Zhang, Wei Jiang, Shiyin Lu, Tianbao Yang:
Revisiting Smoothed Online Learning. CoRR abs/2102.06933 (2021) - [i79]Guanghui Wang, Yuanyu Wan, Tianbao Yang, Lijun Zhang:
Online Convex Optimization with Continuous Switching Constraint. CoRR abs/2103.11370 (2021) - [i78]Qi Qi, Youzhi Luo, Zhao Xu, Shuiwang Ji, Tianbao Yang:
Stochastic Optimization of Area Under Precision-Recall Curve for Deep Learning with Provable Convergence. CoRR abs/2104.08736 (2021) - [i77]Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang:
On Stochastic Moving-Average Estimators for Non-Convex Optimization. CoRR abs/2104.14840 (2021) - [i76]Zhishuai Guo, Tianbao Yang:
Randomized Stochastic Variance-Reduced Methods for Stochastic Bilevel Optimization. CoRR abs/2105.02266 (2021) - [i75]Lijun Zhang, Guanghui Wang, Jinfeng Yi, Tianbao Yang:
A Simple yet Universal Strategy for Online Convex Optimization. CoRR abs/2105.03681 (2021) - [i74]Yunwen Lei, Zhenhuan Yang, Tianbao Yang, Yiming Ying:
Stability and Generalization of Stochastic Gradient Methods for Minimax Problems. CoRR abs/2105.03793 (2021) - [i73]Bokun Wang, Zhuoning Yuan, Yiming Ying, Tianbao Yang:
Memory-based Optimization Methods for Model-Agnostic Meta-Learning. CoRR abs/2106.04911 (2021) - [i72]Guanghui Wang, Ming Yang, Lijun Zhang, Tianbao Yang:
Momentum Accelerates the Convergence of Stochastic AUPRC Maximization. CoRR abs/2107.01173 (2021) - [i71]Tianbao Yang:
Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities. CoRR abs/2111.02400 (2021) - [i70]Zhenhuan Yang, Yunwen Lei, Puyu Wang, Tianbao Yang, Yiming Ying:
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning. CoRR abs/2111.12050 (2021) - [i69]Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang:
A Novel Convergence Analysis for Algorithms of the Adam Family. CoRR abs/2112.03459 (2021) - [i68]Dixian Zhu, Tianbao Yang:
A Unified DRO View of Multi-class Loss Functions with top-N Consistency. CoRR abs/2112.14869 (2021) - 2020
- [j17]Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang:
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints. J. Mach. Learn. Res. 21: 143:1-143:45 (2020) - [j16]Soumitra Pal
Hybrid-DCA: A double asynchronous approach for stochastic dual coordinate ascent. J. Parallel Distributed Comput. 143: 47-66 (2020) - [j15]Tianbao Yang
High-dimensional model recovery from random sketched data by exploring intrinsic sparsity. Mach. Learn. 109(5): 899-938 (2020) - [c92]Dixian Zhu, Dongjin Song, Yuncong Chen, Cristian Lumezanu, Wei Cheng, Bo Zong, Jingchao Ni, Takehiko Mizoguchi, Tianbao Yang, Haifeng Chen:
Deep Unsupervised Binary Coding Networks for Multivariate Time Series Retrieval. AAAI 2020: 1403-1411 - [c91]Pingbo Pan, Ping Liu, Yan Yan, Tianbao Yang, Yi Yang:
Adversarial Localized Energy Network for Structured Prediction. AAAI 2020: 5347-5354 - [c90]Lijun Zhang, Shiyin Lu, Tianbao Yang:
Minimizing Dynamic Regret and Adaptive Regret Simultaneously. AISTATS 2020: 309-319 - [c89]Qi Qi, Yan Yan, Zixuan Wu, Xiaoyu Wang
A Simple and Effective Framework for Pairwise Deep Metric Learning. ECCV (27) 2020: 375-391 - [c88]Zhuoning Yuan, Zhishuai Guo, Xiaotian Yu, Xiaoyu Wang
Accelerating Deep Learning with Millions of Classes. ECCV (23) 2020: 711-726 - [c87]Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, Tianbao Yang:
Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets. ICLR 2020 - [c86]Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang:
Stochastic AUC Maximization with Deep Neural Networks. ICLR 2020 - [c85]Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang:
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. ICML 2020: 3864-3874 - [c84]Runchao Ma, Qihang Lin, Tianbao Yang:
Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints. ICML 2020: 6554-6564 - [c83]Yan Yan, Yi Xu, Lijun Zhang, Xiaoyu Wang, Tianbao Yang:
Stochastic Optimization for Non-convex Inf-Projection Problems. ICML 2020: 10660-10669 - [c82]Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang:
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. NeurIPS 2020 - [c81]Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing:
Improved Schemes for Episodic Memory-based Lifelong Learning. NeurIPS 2020 - [c80]Mingrui Liu, Wei Zhang, Youssef Mroueh, Xiaodong Cui, Jarret Ross, Tianbao Yang, Payel Das:
A Decentralized Parallel Algorithm for Training Generative Adversarial Nets. NeurIPS 2020 - [i67]Lijun Zhang, Shiyin Lu, Tianbao Yang:
Minimizing Dynamic Regret and Adaptive Regret Simultaneously. CoRR abs/2002.02085 (2020) - [i66]Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang:
Sharp Analysis of Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. CoRR abs/2002.05309 (2020) - [i65]Zhishuai Guo, Zixuan Wu, Yan Yan, Xiaoyu Wang, Tianbao Yang:
Revisiting SGD with Increasingly Weighted Averaging: Optimization and Generalization Perspectives. CoRR abs/2003.04339 (2020) - [i64]Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang:
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. CoRR abs/2005.02426 (2020) - [i63]Zhishuai Guo, Zhuoning Yuan, Yan Yan
Fast Objective and Duality Gap Convergence for Non-convex Strongly-concave Min-max Problems. CoRR abs/2006.06889 (2020) - [i62]Yan Yan
Nearly Optimal Robust Method for Convex Compositional Problems with Heavy-Tailed Noise. CoRR abs/2006.10095 (2020) - [i61]Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang:
A Practical Online Method for Distributionally Deep Robust Optimization. CoRR abs/2006.10138 (2020) - [i60]Daoming Lyu, Qi Qi, Mohammad Ghavamzadeh, Hengshuai Yao, Tianbao Yang, Bo Liu:
Variance-Reduced Off-Policy Memory-Efficient Policy Search. CoRR abs/2009.06548 (2020) - [i59]Mingrui Liu, Wei Zhang, Francesco Orabona, Tianbao Yang:
Adam+: A Stochastic Method with Adaptive Variance Reduction. CoRR abs/2011.11985 (2020) - [i58]Zhuoning Yuan, Yan Yan, Milan Sonka, Tianbao Yang:
Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification. CoRR abs/2012.03173 (2020) - [i57]Qi Qi, Yi Xu, Rong Jin, Wotao Yin, Tianbao Yang:
Attentional Biased Stochastic Gradient for Imbalanced Classification. CoRR abs/2012.06951 (2020)
2010 – 2019
- 2019
- [j14]Tianbao Yang:
Advancing non-convex and constrained learning: challenges and opportunities. AI Matters 5(3): 29-39 (2019) - [j13]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion. J. Mach. Learn. Res. 20: 97:1-97:22 (2019) - [j12]Tianbao Yang
A simple homotopy proximal mapping algorithm for compressive sensing. Mach. Learn. 108(6): 1019-1056 (2019) - [c79]Dixian Zhu, Zhe Li, Xiaoyu Wang, Boqing Gong, Tianbao Yang:
A Robust Zero-Sum Game Framework for Pool-based Active Learning. AISTATS 2019: 517-526 - [c78]Jian Ren, Zhe Li, Jianchao Yang, Ning Xu, Tianbao Yang, David J. Foran:
EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching From Scratch. CVPR 2019: 9059-9068 - [c77]Zaiyi Chen, Zhuoning Yuan, Jinfeng Yi, Bowen Zhou, Enhong Chen, Tianbao Yang:
Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions. ICLR (Poster) 2019 - [c76]Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang:
Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number. ICML 2019: 1102-1111 - [c75]Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang:
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence. ICML 2019: 6942-6951 - [c74]Yi Xu, Zhuoning Yuan, Sen Yang, Rong Jin, Tianbao Yang:
On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Minimization. IJCAI 2019: 4003-4009 - [c73]Zhuoning Yuan, Yan Yan, Rong Jin, Tianbao Yang:
Stagewise Training Accelerates Convergence of Testing Error Over SGD. NeurIPS 2019: 2604-2614 - [c72]Yi Xu, Rong Jin, Tianbao Yang:
Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems. NeurIPS 2019: 2626-2636 - [c71]Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang:
Learning with Non-Convex Truncated Losses by SGD. UAI 2019: 701-711 - [i56]Yan Yan
Stochastic Primal-Dual Algorithms with Faster Convergence than O(1/√T) for Problems without Bilinear Structure. CoRR abs/1904.10112 (2019) - [i55]Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang:
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints. CoRR abs/1908.03077 (2019) - [i54]Yan Yan, Yi Xu, Lijun Zhang, Xiaoyu Wang, Tianbao Yang:
Stochastic Optimization for Non-convex Inf-Projection Problems. CoRR abs/1908.09941 (2019) - [i53]Mingrui Liu, Zhuoning Yuan, Yiming Ying, Tianbao Yang:
Stochastic AUC Maximization with Deep Neural Networks. CoRR abs/1908.10831 (2019) - [i52]Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing:
Learning with Long-term Remembering: Following the Lead of Mixed Stochastic Gradient. CoRR abs/1909.11763 (2019) - [i51]Mingrui Liu, Youssef Mroueh, Wei Zhang, Xiaodong Cui, Jerret Ross, Tianbao Yang, Payel Das:
Decentralized Parallel Algorithm for Training Generative Adversarial Nets. CoRR abs/1910.12999 (2019) - [i50]Qi Qi, Yan Yan, Zixuan Wu, Xiaoyu Wang, Tianbao Yang:
a simple and effective framework for pairwise deep metric learning. CoRR abs/1912.11194 (2019) - [i49]Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, Tianbao Yang:
Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets. CoRR abs/1912.11940 (2019) - 2018
- [j11]Dixian Zhu
A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization. Big Data Cogn. Comput. 2(1): 5 (2018) - [j10]Tianbao Yang, Qihang Lin:
RSG: Beating Subgradient Method without Smoothness and Strong Convexity. J. Mach. Learn. Res. 19: 6:1-6:33 (2018) - [c70]Tianbao Yang, Zhe Li, Lijun Zhang:
A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer. AISTATS 2018: 445-453 - [c69]Zihao Cheng, Dong Yue, Songlin Hu, Tianbao Yang, Lei Chen:
An Indirect-Direct event-triggered mechanism for networked control system against DoS attacks. ANZCC 2018: 27-32 - [c68]Yandong Li, Liqiang Wang
How Local Is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization. ECCV (8) 2018: 156-174 - [c67]Aidean Sharghi
Improving Sequential Determinantal Point Processes for Supervised Video Summarization. ECCV (3) 2018: 533-550 - [c66]Zaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang:
SADAGRAD: Strongly Adaptive Stochastic Gradient Methods. ICML 2018: 912-920 - [c65]Qihang Lin, Runchao Ma, Tianbao Yang:
Level-Set Methods for Finite-Sum Constrained Convex Optimization. ICML 2018: 3118-3127 - [c64]Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, Tianbao Yang:
Fast Stochastic AUC Maximization with O(1/n)-Convergence Rate. ICML 2018: 3195-3203 - [c63]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Dynamic Regret of Strongly Adaptive Methods. ICML 2018: 5877-5886 - [c62]Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang:
A Unified Analysis of Stochastic Momentum Methods for Deep Learning. IJCAI 2018: 2955-2961 - [c61]Xiaotian Yu, Irwin King
A Generic Approach for Accelerating Stochastic Zeroth-Order Convex Optimization. IJCAI 2018: 3040-3046 - [c60]Zhuoning Yuan, Xun Zhou, Tianbao Yang:
Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data. KDD 2018: 984-992 - [c59]Xiaoxuan Zhang, Mingrui Liu, Xun Zhou, Tianbao Yang:
Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization. NeurIPS 2018: 3893-3903 - [c58]Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang:
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions. NeurIPS 2018: 4683-4694 - [c57]Mingrui Liu, Zhe Li, Xiaoyu Wang, Jinfeng Yi, Tianbao Yang:
Adaptive Negative Curvature Descent with Applications in Non-convex Optimization. NeurIPS 2018: 4858-4867 - [c56]Yi Xu, Rong Jin, Tianbao Yang:
First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time. NeurIPS 2018: 5535-5545 - [r2]Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu:
Combining Link and Content for Community Detection. Encyclopedia of Social Network Analysis and Mining. 2nd Ed. 2018 - [i48]Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang:
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions. CoRR abs/1805.04577 (2018) - [i47]Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang:
Learning with Non-Convex Truncated Losses by SGD. CoRR abs/1805.07880 (2018) - [i46]Zhe Li, Xuehan Xiong, Zhou Ren, Ning Zhang, Xiaoyu Wang, Tianbao Yang:
An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints. CoRR abs/1806.00851 (2018) - [i45]Jian Ren, Zhe Li, Jianchao Yang, Ning Xu, Tianbao Yang, David J. Foran:
EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching. CoRR abs/1806.01940 (2018) - [i44]Yandong Li, Liqiang Wang, Tianbao Yang, Boqing Gong:
How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization. CoRR abs/1807.04219 (2018) - [i43]Aidean Sharghi, Ali Borji, Chengtao Li, Tianbao Yang, Boqing Gong:
Improving Sequential Determinantal Point Processes for Supervised Video Summarization. CoRR abs/1807.10957 (2018) - [i42]Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang:
A Unified Analysis of Stochastic Momentum Methods for Deep Learning. CoRR abs/1808.10396 (2018) - [i41]Pingbo Pan, Yan Yan, Tianbao Yang, Yi Yang:
Learning Discriminators as Energy Networks in Adversarial Learning. CoRR abs/1810.01152 (2018) - [i40]Hassan Rafique, Mingrui Liu, Qihang Lin, Tianbao Yang:
Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning. CoRR abs/1810.02060 (2018) - [i39]Tianbao Yang, Yan Yan, Zhuoning Yuan, Rong Jin:
Why Does Stagewise Training Accelerate Convergence of Testing Error Over SGD? CoRR abs/1812.03934 (2018) - 2017
- [j9]Jason D. Lee, Qihang Lin, Tengyu Ma, Tianbao Yang:
Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement. J. Mach. Learn. Res. 18: 122:1-122:43 (2017) - [c55]Zhe Li, Tianbao Yang, Lijun Zhang, Rong Jin:
A Two-Stage Approach for Learning a Sparse Model with Sharp Excess Risk Analysis. AAAI 2017: 2224-2230 - [c54]Yi Xu, Haiqin Yang, Lijun Zhang, Tianbao Yang:
Efficient Non-Oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee. AAAI 2017: 2796-2802 - [c53]Yan Yan, Tianbao Yang, Yi Yang, Jianhui Chen:
A Framework of Online Learning with Imbalanced Streaming Data. AAAI 2017: 2817-2823 - [c52]Lijun Zhang, Tianbao Yang, Rong Jin:
Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$- and $O(1/n^2)$-type of Risk Bounds. COLT 2017: 1954-1979 - [c51]Yi Xu, Qihang Lin, Tianbao Yang:
Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence. ICML 2017: 3821-3830 - [c50]Tianbao Yang, Qihang Lin, Lijun Zhang:
A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates. ICML 2017: 3901-3910 - [c49]Yichi Xiao, Zhe Li, Tianbao Yang, Lijun Zhang:
SVD-free Convex-Concave Approaches for Nuclear Norm Regularization. IJCAI 2017: 3126-3132 - [c48]Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou:
Improved Dynamic Regret for Non-degenerate Functions. NIPS 2017: 732-741 - [c47]Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang:
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization. NIPS 2017: 1267-1277 - [c46]Mingrui Liu, Tianbao Yang:
Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition. NIPS 2017: 3104-3114 - [c45]Yi Xu, Qihang Lin, Tianbao Yang:
Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter. NIPS 2017: 3277-3287 - [p1]Chuang Gan, Tianbao Yang, Boqing Gong:
A Multisource Domain Generalization Approach to Visual Attribute Detection. Domain Adaptation in Computer Vision Applications 2017: 277-289 - [i38]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Strongly Adaptive Regret Implies Optimally Dynamic Regret. CoRR abs/1701.07570 (2017) - [i37]Lijun Zhang, Tianbao Yang, Rong Jin:
Empirical Risk Minimization for Stochastic Convex Optimization: O(1/n)- and O(1/n2)-type of Risk Bounds. CoRR abs/1702.02030 (2017) - [i36]Zhe Li, Xiaoyu Wang, Xutao Lv, Tianbao Yang:
SEP-Nets: Small and Effective Pattern Networks. CoRR abs/1706.03912 (2017) - [i35]Tianbao Yang, Zhe Li, Lijun Zhang:
A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer. CoRR abs/1709.02909 (2017) - [i34]Mingrui Liu, Tianbao Yang:
Stochastic Non-convex Optimization with Strong High Probability Second-order Convergence. CoRR abs/1710.09447 (2017) - 2016
- [j8]Tianbao Yang, Rong Jin, Shenghuo Zhu, Qihang Lin:
On Data Preconditioning for Regularized Loss Minimization. Mach. Learn. 103(1): 57-79 (2016) - [c44]Zhe Li, Tianbao Yang, Lijun Zhang, Rong Jin:
Fast and Accurate Refined Nyström-Based Kernel SVM. AAAI 2016: 1830-1836 - [c43]Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou:
Stochastic Optimization for Kernel PCA. AAAI 2016: 2315-2322 - [c42]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Sparse Learning for Large-Scale and High-Dimensional Data: A Randomized Convex-Concave Optimization Approach. ALT 2016: 83-97 - [c41]Chuang Gan, Tianbao Yang, Boqing Gong:
Learning Attributes Equals Multi-Source Domain Generalization. CVPR 2016: 87-97 - [c40]Lijun Zhang, Tianbao Yang, Rong Jin, Yichi Xiao, Zhi-Hua Zhou:
Online Stochastic Linear Optimization under One-bit Feedback. ICML 2016: 392-401 - [c39]Tianbao Yang, Lijun Zhang, Rong Jin, Jinfeng Yi:
Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient. ICML 2016: 449-457 - [c38]Xiaoxuan Zhang, Tianbao Yang, Padmini Srinivasan
Online Asymmetric Active Learning with Imbalanced Data. KDD 2016: 2055-2064 - [c37]Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang:
Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/\epsilon). NIPS 2016: 1208-1216 - [c36]Zhe Li, Boqing Gong, Tianbao Yang:
Improved Dropout for Shallow and Deep Learning. NIPS 2016: 2523-2531 - [c35]Jianhui Chen, Tianbao Yang, Qihang Lin, Lijun Zhang, Yi Chang:
Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections. UAI 2016 - [i33]Zhe Li, Boqing Gong, Tianbao Yang:
Improved Dropout for Shallow and Deep Learning. CoRR abs/1602.02220 (2016) - [i32]Chuang Gan, Tianbao Yang, Boqing Gong:
Learning Attributes Equals Multi-Source Domain Generalization. CoRR abs/1605.00743 (2016) - [i31]Tianbao Yang, Lijun Zhang, Rong Jin, Jinfeng Yi:
Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient. CoRR abs/1605.04638 (2016) - [i30]Yi Xu, Qihang Lin, Tianbao Yang:
Accelerate Stochastic Subgradient Method by Leveraging Local Error Bound. CoRR abs/1607.01027 (2016) - [i29]Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou:
Improved dynamic regret for non-degeneracy functions. CoRR abs/1608.03933 (2016) - [i28]Soumitra Pal, Tingyang Xu, Tianbao Yang, Sanguthevar Rajasekaran, Jinbo Bi:
Hybrid-DCA: A Double Asynchronous Approach for Stochastic Dual Coordinate Ascent. CoRR abs/1610.07184 (2016) - [i27]Yi Xu, Haiqin Yang, Lijun Zhang, Tianbao Yang:
Efficient Non-oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee. CoRR abs/1612.01663 (2016) - 2015
- [j7]Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Shenghuo Zhu:
An efficient primal dual prox method for non-smooth optimization. Mach. Learn. 98(3): 369-406 (2015) - [c34]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Online Bandit Learning for a Special Class of Non-Convex Losses. AAAI 2015: 3158-3164 - [c33]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
A Simple Homotopy Algorithm for Compressive Sensing. AISTATS 2015 - [c32]Saining Xie, Tianbao Yang, Xiaoyu Wang
Hyper-class augmented and regularized deep learning for fine-grained image classification. CVPR 2015: 2645-2654 - [c31]Syed Shabih Hasan, Ryan Brummet, Octav Chipara
In-Situ Measurement and Prediction of Hearing Aid Outcomes Using Mobile Phones. ICHI 2015: 525-534 - [c30]Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu:
An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection. ICML 2015: 135-143 - [c29]Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu:
Theory of Dual-sparse Regularized Randomized Reduction. ICML 2015: 305-314 - [c28]Jinfeng Yi, Lijun Zhang, Tianbao Yang, Wei Liu
An Efficient Semi-Supervised Clustering Algorithm with Sequential Constraints. KDD 2015: 1405-1414 - [c27]Tianbao Yang, Qihang Lin, Rong Jin:
Big Data Analytics: Optimization and Randomization. KDD 2015: 2327 - [i26]Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu:
Theory of Dual-sparse Regularized Randomized Reduction. CoRR abs/1504.03991 (2015) - [i25]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Analysis of Nuclear Norm Regularization for Full-rank Matrix Completion. CoRR abs/1504.06817 (2015) - [i24]Tianbao Yang, Lijun Zhang, Qihang Lin, Rong Jin:
Fast Sparse Least-Squares Regression with Non-Asymptotic Guarantees. CoRR abs/1507.05185 (2015) - [i23]Adams Wei Yu, Qihang Lin, Tianbao Yang:
Doubly Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization with Factorized Data. CoRR abs/1508.03390 (2015) - [i22]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Online Stochastic Linear Optimization under One-bit Feedback. CoRR abs/1509.07728 (2015) - [i21]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Stochastic Proximal Gradient Descent for Nuclear Norm Regularization. CoRR abs/1511.01664 (2015) - [i20]Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou:
Sparse Learning for Large-scale and High-dimensional Data: A Randomized Convex-concave Optimization Approach. CoRR abs/1511.03766 (2015) - 2014
- [j6]Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Shenghuo Zhu:
Regret bounded by gradual variation for online convex optimization. Mach. Learn. 95(2): 183-223 (2014) - [j5]Lijun Zhang, Mehrdad Mahdavi, Rong Jin, Tianbao Yang, Shenghuo Zhu:
Random Projections for Classification: A Recovery Approach. IEEE Trans. Inf. Theory 60(11): 7300-7316 (2014) - [c26]Jianhui Chen, Tianbao Yang, Shenghuo Zhu:
Efficient Low-Rank Stochastic Gradient Descent Methods for Solving Semidefinite Programs. AISTATS 2014: 122-130 - [c25]Tianbao Yang, Rong Jin:
Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities. NIPS 2014: 262-270 - [r1]Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu:
Combining Link and Content for Community Detection. Encyclopedia of Social Network Analysis and Mining 2014: 190-201 - [i19]Tianbao Yang, Rong Jin, Shenghuo Zhu:
On Data Preconditioning for Regularized Loss Minimization. CoRR abs/1408.3115 (2014) - [i18]Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu:
A Simple Homotopy Proximal Mapping for Compressive Sensing. CoRR abs/1412.1205 (2014) - [i17]Xiaoyu Wang, Tianbao Yang, Guobin Chen, Yuanqing Lin:
Object-centric Sampling for Fine-grained Image Classification. CoRR abs/1412.3161 (2014) - 2013
- [j4]Steven C. H. Hoi
Online Multiple Kernel Classification. Mach. Learn. 90(2): 289-316 (2013) - [j3]Rong Jin, Tianbao Yang, Mehrdad Mahdavi, Yufeng Li, Zhi-Hua Zhou:
Improved Bounds for the Nyström Method With Application to Kernel Classification. IEEE Trans. Inf. Theory 59(10): 6939-6949 (2013) - [c24]Tianbao Yang, Prakash Mandayam Comar, Linli Xu:
Community detection by popularity based models for authored networked data. ASONAM 2013: 74-81 - [c23]Lijun Zhang, Mehrdad Mahdavi, Rong Jin, Tianbao Yang, Shenghuo Zhu:
Recovering the Optimal Solution by Dual Random Projection. COLT 2013: 135-157 - [c22]Lijun Zhang, Tianbao Yang, Rong Jin, Xiaofei He:
O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions. ICML (3) 2013: 1121-1129 - [c21]Tianbao Yang, Lei Wu, Piero P. Bonissone
A Directed Inference Approach towards Multi-class Multi-model Fusion. MCS 2013: 352-363 - [c20]Tianbao Yang:
Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent. NIPS 2013: 629-637 - [c19]Mehrdad Mahdavi, Tianbao Yang, Rong Jin:
Stochastic Convex Optimization with Multiple Objectives. NIPS 2013: 1115-1123 - [i16]Rong Jin, Tianbao Yang, Mehrdad Mahdavi:
Sparse Multiple Kernel Learning with Geometric Convergence Rate. CoRR abs/1302.0315 (2013) - [i15]Lijun Zhang, Tianbao Yang, Rong Jin, Xiaofei He:
O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions. CoRR abs/1304.0740 (2013) - [i14]Rong Jin, Tianbao Yang, Shenghuo Zhu:
A New Analysis of Compressive Sensing by Stochastic Proximal Gradient Descent. CoRR abs/1304.4680 (2013) - [i13]Tianbao Yang, Lijun Zhang:
Efficient Stochastic Gradient Descent for Strongly Convex Optimization. CoRR abs/1304.5504 (2013) - [i12]Tianbao Yang, Shenghuo Zhu, Rong Jin, Yuanqing Lin:
On Theoretical Analysis of Distributed Stochastic Dual Coordinate Ascent. CoRR abs/1312.1031 (2013) - 2012
- [j2]Mehrdad Mahdavi, Rong Jin, Tianbao Yang:
Trading regret for efficiency: online convex optimization with long term constraints. J. Mach. Learn. Res. 13: 2503-2528 (2012) - [c18]Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Jinfeng Yi, Steven C. H. Hoi:
Online Kernel Selection: Algorithms and Evaluations. AAAI 2012: 1197-1203 - [c17]Jinfeng Yi, Tianbao Yang, Rong Jin, Anil K. Jain, Mehrdad Mahdavi:
Robust Ensemble Clustering by Matrix Completion. ICDM 2012: 1176-1181 - [c16]Ming Ji, Tianbao Yang, Binbin Lin, Rong Jin, Jiawei Han:
A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound. ICML 2012 - [c15]Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Lijun Zhang, Yang Zhou:
Multiple Kernel Learning from Noisy Labels by Stochastic Programming. ICML 2012 - [c14]Tianbao Yang, Yufeng Li, Mehrdad Mahdavi, Rong Jin, Zhi-Hua Zhou:
Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison. NIPS 2012: 485-493 - [c13]Mehrdad Mahdavi, Tianbao Yang, Rong Jin, Shenghuo Zhu, Jinfeng Yi:
Stochastic Gradient Descent with Only One Projection. NIPS 2012: 503-511 - [c12]Jinfeng Yi, Rong Jin, Anil K. Jain, Shaili Jain, Tianbao Yang:
Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning. NIPS 2012: 1781-1789 - [c11]Tianbao Yang, Rong Jin, Anil K. Jain:
Learning kernel combination from noisy pairwise constraints. SSP 2012: 752-755 - [c10]Chao-Kai Chiang, Tianbao Yang, Chia-Jung Lee, Mehrdad Mahdavi, Chi-Jen Lu, Rong Jin, Shenghuo Zhu:
Online Optimization with Gradual Variations. COLT 2012: 6.1-6.20 - [i11]Tianbao Yang, Rong Jin, Mehrdad Mahdavi, Shenghuo Zhu:
An Efficient Primal-Dual Prox Method for Non-Smooth Optimization. CoRR abs/1201.5283 (2012) - [i10]Mehrdad Mahdavi, Tianbao Yang, Rong Jin:
Efficient Constrained Regret Minimization. CoRR abs/1205.2265 (2012) - [i9]Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu:
A Bayesian Framework for Community Detection Integrating Content and Link. CoRR abs/1205.2603 (2012) - [i8]Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Lijun Zhang, Yang Zhou:
Multiple Kernel Learning from Noisy Labels by Stochastic Programming. CoRR abs/1206.4629 (2012) - [i7]Mehrdad Mahdavi, Tianbao Yang, Rong Jin:
An Improved Bound for the Nystrom Method for Large Eigengap. CoRR abs/1209.0001 (2012) - [i6]Lijun Zhang, Mehrdad Mahdavi, Rong Jin, Tianbao Yang:
Recovering Optimal Solution by Dual Random Projection. CoRR abs/1211.3046 (2012) - [i5]Mehrdad Mahdavi, Tianbao Yang, Rong Jin:
Online Stochastic Optimization with Multiple Objectives. CoRR abs/1211.6013 (2012) - [i4]Michinari Momma, Yun Chi, Yuanqing Lin, Shenghuo Zhu, Tianbao Yang:
Influence Analysis in the Blogosphere. CoRR abs/1212.5863 (2012) - 2011
- [j1]Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, Rong Jin:
Detecting communities and their evolutions in dynamic social networks - a Bayesian approach. Mach. Learn. 82(2): 157-189 (2011) - [c9]Peilin Zhao, Steven C. H. Hoi, Rong Jin, Tianbao Yang:
Online AUC Maximization. ICML 2011: 233-240 - [c8]Wei Tong, Fengjie Li, Tianbao Yang, Rong Jin, Anil K. Jain:
A kernel density based approach for large scale image retrieval. ICMR 2011: 28 - [i3]Rong Jin, Tianbao Yang, Mehrdad Mahdavi:
Improved Bound for the Nystrom's Method and its Application to Kernel Classification. CoRR abs/1111.2262 (2011) - [i2]Mehrdad Mahdavi, Rong Jin, Tianbao Yang:
Trading Regret for Efficiency: Online Convex Optimization with Long Term Constraints. CoRR abs/1111.6082 (2011) - [i1]Tianbao Yang, Rong Jin, Mehrdad Mahdavi:
Regret Bound by Variation for Online Convex Optimization. CoRR abs/1111.6337 (2011) - 2010
- [c7]Rong Jin, Steven C. H. Hoi
Online Multiple Kernel Learning: Algorithms and Mistake Bounds. ALT 2010: 390-404 - [c6]Tianbao Yang, Rong Jin, Anil K. Jain:
Learning from Noisy Side Information by Generalized Maximum Entropy Model. ICML 2010: 1199-1206 - [c5]Tianbao Yang, Rong Jin, Anil K. Jain, Yang Zhou, Wei Tong:
Unsupervised transfer classification: application to text categorization. KDD 2010: 1159-1168 - [c4]Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, Rong Jin:
Directed Network Community Detection: A Popularity and Productivity Link Model. SDM 2010: 742-753
2000 – 2009
- 2009
- [c3]Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu:
Combining link and content for community detection: a discriminative approach. KDD 2009: 927-936 - [c2]Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, Rong Jin:
A Bayesian Approach Toward Finding Communities and Their Evolutions in Dynamic Social Networks. SDM 2009: 990-1001 - [c1]Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu:
A Bayesian Framework for Community Detection Integrating Content and Link. UAI 2009: 615-622
Coauthor Index
[j22] [c127] [i83] [c96] [i77] [i69] [j15] [i61] [i57] [j13] [j12] [c75] [c74] [c73] [c72] [c71] [c63] [c58] [c56] [r2] [i48] [i47] [i39] [c55] [c52] [c48] [i38] [i37] [j8] [c44] [c43] [c42] [c40] [c39] [i31] [i29] [j7] [c34] [c33] [c30] [c29] [c27] [i26] [i25] [i24] [i22] [i21] [i20] [j6] [j5] [c25] [r1] [i19] [i18] [j4] [j3] [c23] [c22] [c19] [i16] [i15] [i14] [i12] [j2] [c18] [c17] [c16] [c15] [c14] [c13] [c12] [c11] [c10] [i11] [i10] [i9] [i8] [i7] [i6] [i5] [j1] [c9] [c8] [i3] [i2] [i1] [c7] [c6] [c5] [c4] [c3] [c2] [c1]
[c132] [i125] [c126] [c125] [c123] [c118] [i113] [i109] [i108] [i106] [c113] [c111] [c110] [c107] [c106] [c103] [c101] [i98] [i97] [i96] [i90] [i86] [c95] [c93] [i80] [i79] [i75] [i72] [j15] [c90] [c83] [i67] [j13] [j12] [i56] [i54] [c70] [c63] [c58] [i48] [c55] [c54] [c52] [c50] [c49] [c48] [i38] [i37] [i35] [c44] [c43] [c42] [c40] [c39] [c35] [i31] [i29] [i27] [c34] [c33] [c30] [c29] [c28] [i26] [i25] [i24] [i22] [i21] [i20] [j5] [i18] [c23] [c22] [i15] [i13] [c15] [i8] [i6]
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